Real-time data processing pipeline and pacing control systems and methods

ABSTRACT

A data processing system includes a transaction bus, a console application in communication with the transaction bus, and a view predictor subsystem in communication with the transaction bus. The transaction bus receives, from a user application executing on a client device, a call for visual information to be provided to the user application. The view predictor subsystem determines a likelihood that the visual information will be viewable within a viewport of the user application, and a plurality of respective values for a plurality of sources of the visual information are computed based on the likelihood and a respective priority for each source. The console application provides to the transaction bus the set of potential sources of the visual information, and the transaction bus selects, based on the computed values, one of the potential sources of the visual information to be the result, which is provided to the user application.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/077,472 (now U.S. Pat. No. 11,188,401) filed on Oct. 22, 2020, whichis a continuation of U.S. patent application Ser. No. 16/037,621 (nowU.S. Pat. No. 10,824,487) filed on Jul. 17, 2018. The contents of eachof the foregoing is/are hereby incorporated by reference into thisapplication as if set forth herein in full.

TECHNICAL FIELD

The present disclosure relates generally to data processing and controlarchitectures and, more particularly, to a real-time data and messageprocessing system and corresponding pacing control methods.

BACKGROUND

Data processing and storage systems that have to address thousands ofsimultaneous transactions within tens or hundreds of milliseconds facesubstantial challenges in scaling while keeping latency at a minimum.These difficulties increase when the results of data processingoperations depend on data that is continuously collected from manyremote clients. Accordingly, there is a need to provide real-time dataprocessing systems with high-performance data storage and streamingarchitectures that address the unique problems arising in these types ofsystems at large scales.

BRIEF SUMMARY

In one aspect, a computer-implemented method comprises the steps of:providing a data processing system comprising (i) a transaction bus,(ii) a console application in communication with the transaction bus,(iii) and a view predictor subsystem in communication with thetransaction bus; receiving, at the transaction bus from a userapplication executing on a client device, a call for visual informationto be provided to the user application; in substantially real-time,prior to returning a result to the user application in response to thecall for visual information: determining, by the view predictorsubsystem, a likelihood that the visual information will be viewablewithin a viewport of the user application; computing a plurality ofrespective values for a plurality of sources of the visual informationbased on the likelihood and a respective priority for each source;providing, by the console application to the transaction bus, the set ofpotential sources of the visual information; and selecting, by thetransaction bus, based on the computed values, one of the potentialsources of the visual information to be the result; and providing theresult, by the transaction bus, to the user application. Other aspectsof the foregoing include corresponding systems and computer-executableinstructions stored on non-transitory storage media.

In one implementation, the data processing system further comprises apublish/subscribe and message queueing subsystem, and the performedoperations further include: receiving, at the publish/subscribe andmessage queueing subsystem, a real-time stream of transactions; creatinga job to aggregate data from the real-time stream of transactions withdata from one or more other streams received at the publish/subscribeand messaging queueing subsystem; and executing the job and receivingthe aggregated data resulting from the execution of the job at thepublish/subscribe and message queueing subsystem; providing at least aportion of the aggregated data as input to a control loop feedbackprocess; and executing the control loop feedback process using the inputand generating a pacing result from the execution.

Various implementations can include any of the following features. Thepacing result can be applied to one or more of the computed values.Executing the control loop feedback process can include executing thecontrol loop feedback process at a first time to generate the pacingresult. Additional jobs can be periodically created and executed toaggregate data received from the streams. The pacing result and at leasta portion of the aggregated data from the additional jobs can beprovided as input to the control loop feedback process at a second,later time to generate an updated pacing result. Computing a particularvalue can include calculating a value that meets a specified goal forproviding visual information to user applications. The visualinformation can include an image or a video.

In another implementation, the performed operations further includetracking over time, for a plurality of visual information items providedto a plurality of user applications, historical viewability informationindicating whether the visual information items were viewable inrespective viewports of the plurality of user applications; andproviding, by the message queueing subsystem to the view predictorsubsystem, the historical viewability information, wherein the viewpredictor subsystem determines the likelihood that the visualinformation will be viewable within a viewport of the user applicationbased on the historical viewability information.

The performed operations can also include providing, by the transactionbus to the user application, in response to the call for visualinformation, software code for execution on the client device, thesoftware code configured to determine when the visual information isviewable within the viewport of the user application.

The details of one or more implementations of the subject matterdescribed in the present specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like reference characters generally refer to the sameparts throughout the different views. Also, the drawings are notnecessarily to scale, emphasis instead generally being placed uponillustrating the principles of the implementations. In the followingdescription, various implementations are described with reference to thefollowing drawings, in which:

FIG. 1 depicts an example high-level system architecture for real-timedata processing pipelines according to an implementation.

FIG. 2 depicts an example web browser window displaying a scrollable webpage and having a defined viewport.

FIG. 3 depicts a data flow diagram of an example method for real-timedata processing based on the viewable status of visual informationwithin a viewport.

FIG. 4 depicts a flowchart of an example method for real-time dataprocessing based on the viewable status of visual information within aviewport.

FIG. 5 depicts example value calculation dynamics based on a predictedview rate of visual information within a viewport.

FIG. 6 depicts an example architecture of a system for real-time streamprocessing to provide data to a control loop feedback algorithm.

FIG. 7 depicts an example data layer of a stream processing systemaccording to an implementation.

FIG. 8 depicts a logical view of real-time structured object dataaggregation using join scripts according to an implementation.

DETAILED DESCRIPTION

FIG. 1 depicts an example high-level system 100 including a real-timedata processing pipeline architecture, according to one implementation.A client device 102, such as a smartphone, laptop, desktop computer,tablet, or other computing device, executes a user application 104, suchas a web browser or desktop or mobile operating system nativeapplication. Upon occurrence of an event on the client device 102 (e.g.,opening user application 104, browsing to a webpage within userapplication 104, etc.), user application 104 makes a call, over anetwork (e.g., the internet, mobile phone network, etc.), to transactionbus 110 within the system 100. The call can include a request for data,for example, a request to receive visual information such as an image orvideo.

Transaction bus 110 executes on one or more physical servers within oneor more data centers, and interfaces with various components withinsystem 100 to cause the performance of real-time data processingoperations in response to the call made by user application 104. Forexample, in response to the call, transaction bus 110 makes a request toview predictor subsystem 114 and receives a response in return. Asfurther described below, view predictor subsystem 114 can determine thelikelihood that the data requested by user application 104 will beviewable to a user. For example, in the case of the data comprising animage and the user application 104 being a web browser, view predictorsubsystem 114 predicts whether the image will appear at some pointwithin the browser's viewport (the area within application 104 visibleto a user), whether as a result of being displayed immediately withinthe viewport or at a later time when the user scrolls down, for example.

Transaction bus 110 makes the viewability likelihood received from viewpredictor subsystem 114 available to console application 118, which usesthe information as input to its decision-making logic. Consoleapplication 118 can retrieve additional data inputs for use in its logicfrom events database 136. In one implementation, console application 118includes individual bidders that are configured as software and/orhardware-based operators that execute bidding logic in onlineadvertising auctions. Such bidders can run on computing devices eachhaving one or more processors and memories to store computer-executableinstructions executable by the processor. In such an implementation,transaction bus 110 makes a request to console application 118 for bidsto serve a creative to the user of user application 104 in an ad auctionand provides the viewability likelihood in the request. The bidders havebidding logic that can take the viewability likelihood into account(along with, e.g., information about the user, client device 102, andother targeting information) when calculating a bid price, which isreturned in a bid response to the transaction bus 110.

Following the request and response interactions with console application118, transaction bus 110 provides the requested data (or a referencethereto) to user application 104, along with code (e.g., JavaScriptfunctionality) that is executed by user application 104 and determineswhen the requested data is or becomes viewable within user application104. Periodically and/or upon the data becoming viewable, the code canexecute a callback to transmit viewability information regarding thedata to transaction bus 110. Transaction bus 110 can also notify consoleapplication 118 of the viewability data received from user application104.

In conjunction with the operations described above, data associated withthese operations can be captured from transaction bus 110 and consoleapplication 118, and processed by logging subsystem 112 for recording inlog database 126, which can be, for example, one or more high-volume,high-performance databases made available through HPE Vertica or anotherprovider. The processed data can then be retrieved from log database 126by message queueing subsystem 128, which can include a streaming,distributed publish/subscribe (pub/sub) and messaging service, such asApache Kafka™. Message queueing subsystem 128 can provide at-least-oncemessaging guarantees to ensure no messages are lost when a fault occurs,and highly available partitions such that a stream's partitions continueto be available even when a machine fails. Message queueing subsystem128 makes processed data regarding historical events from log database126 available to view predictor subsystem 114, for use in determiningviewability likelihood, as further described below.

Ultimately, the data processing architecture depicted in FIG. 1 allowsfor predicted viewability data to be considered at large scale (e.g.,thousands of simultaneous transactions or more) in a brief period oftime (substantially real-time, e.g., within 1 second, 500 milliseconds,etc.) between transaction bus 110 receiving the call from userapplication 104 and returning data to it. In the context of an onlineadvertising platform, this means that many simultaneous auctions toserve creatives to users can incorporate viewability data in the biddingprocess, while still completing the auctions and serving the creativeson the order of milliseconds, avoiding undesirable delays from theusers' perspective.

Referring now to FIG. 2 , a viewport 210 of a user interface (e.g., ofuser application 104) is an area of the user interface within whichcontent presented by the user interface is viewable (visible) to a user.For instance, viewport 210 can be a display area of a web browser window202 displaying a scrollable web page 250. An image or video at thebottom of the web page 250 may not be in the display area, for instance,when the display area shows the top-half of the web page. As shown inFIG. 2 , web browser window 202 displays only portion of a scrollableweb page 250, in viewport 210. The browser window 202 includes avertical scroll bar 204 for scrolling the web page 250 in the verticaldirection, and a horizontal scroll bar 206 for scrolling the web page250 in the horizontal direction. For a defined area 226 (e.g., space foran advertisement) that is within the viewport 210, the area is viewable(in the viewport 210). For a defined area 222 that is beyond theviewport 210 in the vertical direction, or a defined area 224 that isbeyond the viewport 210 in the horizontal direction, such defined areas222 and 224 are not viewable. For a defined area 228 that straddles aborder of the viewport 210 but is partially within the viewport 210, thearea 228 is considered viewable in some implementations. In furtherimplementations, a defined area is considered viewable if at least 50%of the area is within the viewport 210 for at least one second or otherminimum time period. In some implementations, a defined area isconsidered as viewable only if the entire area is within the viewport210 for any amount of time. In another implementation, if the definedarea includes a video, the defined area is considered viewable if atleast 50% of the area is viewable for at least two seconds or otherminimum time period, or if 100% of the area is in view for at least 50%of the duration of the video. In some instances, the definition of“viewable” is a custom definition that can be set individually by usersof the data processing system.

Referring now to the example use case of an online advertising platformimplementing real-time bidding auctions to serve advertisements toconsumers, when a creative of a winning bidder is served to a userapplication (i.e., an impression), but the ad space is not in theviewport of the application, the creative in the ad space is also not inview of the user, and thus has little or no effect in advertising to theuser for the winning bidder. It is desirable for bidders on an auctionof an available ad space to know, before submitting bids on the adspace, the likelihood that the ad space will be viewable for a userafter a creative is served to the ad space (i.e., the predictedviewability). When a notification of an available ad space is receivedby transaction bus 110 from user application 104 client device 102,transaction bus 110 can transmit instructions (e.g., a viewabilityscript) to user application 104 to determine real-time or periodicviewability data of the ad space. The determined viewability data isthen sent from user application 104 to transaction bus 110 and stored indatabase 126 for future calculations by view predictor subsystem 114,using a prediction model. A prediction model can be a mathematicalformula that predicts the viewability for an ad space based historicalviewability information and, in some instances, real-time viewabilitydata received from the view script executing on user application 104, aswell.

Other prediction models are possible. For instance, a prediction modelcan be a data structure (e.g., a graph, a lookup table) that can be usedto predict the viewability of an ad space based on historical and/orreal-time viewability data of the ad space. Particular implementationsconduct an auction on the ad space by sending a bid request to multiplebidders that includes the predicted viewability. In this way, thebidders can submit their respective bids on the auction based on thepredicted viewability.

FIG. 3 is a data flow diagram of an example method for auctioning anavailable ad space 326 based on viewability of the ad space 326, usingthe system described herein. The example method can be implemented usingtransaction bus 110, for example. In FIG. 3 , transaction bus 110receives a notification (an ad call) of ad space 326 that is availablein user application 104 executing on client device 102 (302).Transaction bus 110 sends to client device 102 instructions “bounce.js”(304). The instructions can be JavaScript or Java programming languageinstructions, for example. Other types of instructions are possible. Theinstructions are configured to be executed by client device 102 fordetermining viewability data for ad space 326 (306). Viewability datafor ad space 326 can include an indication of whether ad space 326 iswithin a viewport (“in view”) of user application 104, for example. Forinstance, the instructions can determine whether a Document Object Model(DOM) element corresponding to ad space 326 is within a bounding boxcorresponding to a viewport of user application 104. The instructionscan be executed by user application 104 and determine whether ad space326 is within the viewport, and send back to transaction bus 110 amessage indicating whether ad space 326 is in view within the viewport(308). For instance, the message can include a flag with value of 1indicating that the ad space is in view within the viewport, or value of0 indicating otherwise.

In addition to whether ad space 326 is within a viewport of userapplication 104, the instructions can also determine other viewabilitydata of ad space 326. For instance, the instructions can determine aposition of ad space 326 (e.g., horizontal and vertical distances inpixels from the top left corner of the viewport), and a size (e.g., inpixels) of the viewport, and send back to transaction bus 110 thedetermined position of ad space 326 and size of the viewport. Theposition of ad space 326 relative the size of the viewport can indicatehow probable it is that ad space 326 will be within the viewport at alater time, if ad space 326 is not already within the viewport, forexample. The instructions can send back to transaction bus 110 an errormessage if the instructions cannot determine viewability data of adspace 326 (e.g., when user application 104 is hidden behind another userapplication).

In some implementations and by way of illustration, the instructions cancomprise Javascript code as follows:

getViewabilityData: function(e)  { var t, n, r, o = 0,  i =this.isFriendlyFrame( ); if (!i && void 0 === e.mozInnerScreenY) return{  s: 1 }; if (i)  {  var c = this.injectVirtualElement( );  t =this.getAbsolutePosition(e, c);  for (var d, a, s = e; s != e.top;)  { try { a = s.frameElement  } catch (u)  { return {  s: 4 }  }  if (null=== a) return { s: 5  };  s = s.parent, d =this.getAbsolutePosition(s.parent, a), t = { x: t.x + d.x, y: t.y + d.y }  }  var l = s.innerWidth | | s.document.documentElement.clientWidth || s.document.body.clientWidth,  f = s.innerHeight | |s.document.documentElement.clientHeight | |s.document.body.clientHeight;  r = {  x: l, y: f  }, n =this.isInView(t, l, f) } else void 0 ! == e.mozInnerScreenY && (t = { x: e.mozInnerScreenX − e.screenX - 4,  y: e.mozInnerScreenY −e.screenY - 40 }, r = {  x: screen.width - 8,  y: screen.height - 119 },n = this.isInView(t, r.x, r.y)); return (void 0 !== n && void 0 !==document.hidden && document.hidden | | i && !e.top.document.hasFocus( ))&& (n = !1, o = 3),  {  p: t,  w: r,  v: n, s: o }  }  };  var c =function(n)  {  var r, o = ““;  try { n = e(n, 0); var c = e(new i(n),1),  d = c.getViewabilityData(window); void 0 !== d.v && (o += “&” +n.iv + “=“ + (d.v ? 1 : 0)), void 0 !== d.p && (o += “&” + n.pos + “=“ +Math.round(d.p.x) + “,” + Math.round(d.p.y)), void 0 !== d.w && (o +=“&” + n.ws + “=“ + Math.round(d.w.x) + “,” + Math.round(d.w.y)), r =d.s, window._ anxpv = d  } catch (a) { “undefined” != typeof console, r= 2, t(a, “AnViewModule::executeViewability”)  }  return void 0 !== r &&(o += “&” + n.vs + “=“ + r), “undefined” ! = typeof console, o  }; ;

The viewability data returned by the function above comprises theviewport's width and height, the position of the top left pixel of theads pace (x and y), and a binary flag that indicates whether theposition is within the viewport or not. The position and viewport sizeare used to compute the relative position of the top left pixel bybreaking the screen of the client device into 25 buckets: 5 bucketsalong the height and 5 buckets along the width and determining whichbucket the x and y position falls in. Other divisions of the screen canbe used. The bucketed relative position x and y and the binary flag areused as additional features X in the model described below. Afterreceiving from client device 102 the viewability data determined by theinstructions, transaction bus 110 calculates a predicted viewability forad space 326. Alternatively, transaction bus 110 can calculate apredicted viewability for ad space 326 without the use of viewabilitydata from client device 102 (e.g., the viewability script can beprovided to client device 102 on completion of the auction, instead ofprior to bidding, and be used to gather viewability data for futureviewability tracking and/or billing purposes).

The viewability likelihood can be a probability that ad space 326 ispositioned within the viewport at a time instance after the previoustime instance when the instructions determined the viewability data ofad space 326, for example. More particularly, the viewability can be aprobability that ad space 326 is positioned within the viewport or apart of the view port when a creative is served to the ad space. Theviewability likelihood can also be a value representing the probabilitythat ad space 326 will become viewable at any time.

Transaction bus 110 (through view predictor subsystem 114) can calculatea predicted viewability for ad space 326 using a prediction model of oneor more discrete features or parameters. A feature can be a parameterdescribing ad space 326 (e.g., an ad tag identifier), an ad spaceinventory to which the ad space 326 belongs (e.g., a web address for theweb domain of the ad space inventory), user application 104 (e.g., anapplication identifier), client device 102 (e.g., a device identifier,or a device type identifier), or the user of the client device 102(e.g., a user identifier). A feature can also be a combination of one ormore of these features. For instance, a feature can be a combination ofad space and ad space inventory, a combination of ad space andapplication, or a combination of ad space inventory and application.Other combinations are possible.

By way of illustration, the prediction model can be a logistic functionof one or more features with coefficients for the features as follows:

$p = \frac{1}{1 + e^{- {({\beta_{0} + {X_{1}\beta_{1}} + \;{{.\;.\;.\;{+ \beta_{n}}}X_{n}}})}}}$In the logistic function above, p is a predicted viewability. X₁, . . ., X_(n) are n features. β₀, β₁, . . . , β_(n) are coefficients. Viewpredictor subsystem 114 can receive from view data database 338 (whichcan be, e.g., log database 126 in FIG. 1 ) coefficients of the logisticfunction (310) and the historical view rates for each of the features,and calculate a predicted viewability for ad space 326 using thelogistic function. Viewability can also be predicted as described inU.S. patent application Ser. No. 15/180,913, filed on Jun. 13, 2016, andentitled “Online Ad Auction Based on Predicted Ad Space Viewability,”the entirety of which is incorporated by reference herein.

After calculating a predicted viewability of ad space 326, transactionbus 110 conducts an auction on ad space 326 by sending a bid request tomultiple bidders, for example, bidder C 353, bidder A 351, and bidder D328 (312). In addition to ad space and user information, the bid requestincludes the predicted viewability (e.g., 60%) calculated with theprediction model. In this way, a bidder can determine its bid price forad space 326 based on the predicted viewability. In someimplementations, the bidder can submit a CPM or CPC bid at a pricedeemed appropriate based on the predicted viewability, or the bidder cansubmit a cost-per-view (CPV) bid. In a CPV model, advertisers typicallypay each time their advertisement is viewed by a user. As furtherdescribed below, the bidder can also submit a viewable CPM (vCPM) bid,such that the associated advertiser only pays for ads that becomeviewable.

Transaction bus 110 will calculate a corresponding estimated, orexpected, CPM (eCPM) bid for use in the auction bid ranking. Anestimated CPM (eCPM) bid is a CPV bid multiplied by the predictedviewability. For instance, bidder A 351 may determine a CPV bid price of$0.10 for an ad space of the ad space inventory including ad space 326.Based on the predicted viewability of 60% that the ad space 326 will beviewed by a user, transaction bus 110 will discount the bid price for adspace 326 to $0.06. In some implementations, a seller is able to set aneCPM floor price for their inventory which serves as a minimum bid foreCPM bids. In a second-price auction, the eCPM bid can act as the secondprice in the auction should a non-eCPM bid win. Other methods forbidding for an ad space by a bidder based on a predicted viewability ofthe ad space are possible. For instance, a bidder may forgo submitting abid on the auction if the bidder determines that the predictedviewability is less than a specific threshold (e.g., less than 35%).

If a eCPM bid wins an auction, transaction bus 110 will log thetransaction, except that the transaction will have no cost to the buyeror revenue to the seller. An encoded data structure, which includes thetransaction ID and the CPV price, is then served with the creative and aviewability measurement script (described below). The measurement scriptexecutes in the client devices, determines whether the creativeassociated with the bid was viewable, and sends the measurement resultsback to the transaction manager 112 including the encoded datastructure. If the measurement results indicate that the creative isviewable, the transaction bus 110 will log the cost information for thetransaction and send notification to the bidder that the CPV bid wasviewed and therefore charged.

Bidders can send their respective bid price for ad space 326 totransaction bus 110 (314). In FIG. 3 , transaction bus 110 selects thehighest bid price of $0.11 from bidder D 328, and notifies ad server 364(316). Ad server 364 then sends a creative for bidder D 328 to clientdevice 102 to be served to ad space 326 (318). In addition, transactionbus 102 records transaction information (e.g., winning bid price,winning buyer, ad space 326, time stamp, and so on) in the transactiondata database 334 (which can be, e.g., log database 126 in FIG. 1 )(320).

In some implementations, after the creative for bidder D 328 is servedto ad space 326, instructions (e.g., JavaScript) can determine whetherad space 326 is in view of user application 104, and send back totransaction bus 110 a message indicating whether ad space 326(containing the creative for the bidder D 328) is in view in userapplication 104 (322). The instructions check that an actual ad ispresent (versus just a container for an ad). They also measure accordingto one or more definitions of a “viewable” impression, which generallyis a function of how much of the ad is in-view and for how long. Themeasurement data determined by the instructions is a result with threeoptions: 1) unable to measure, 2) measured not viewable, 3) measuredviewable. Transaction bus 110 can store the in-view data in view datadatabase 338 in reference to ad space 326 (324). Other methods fordetermining whether ad space 326 and the creative for the bidder D 328are in view in user application 104 are possible. For instance, thecreative may send to transaction bus 110 a notification of a click eventwhen the creative is selected by the user. In further implementations, amobile application executing on client device 102 uses a softwaredevelopment kit (SDK) for determining viewability of ad spaces in themobile application's user interface, which data can be sent totransaction bus 110.

A model generator or other software component in data processing system100 can use the in-view data to update the prediction model describedearlier, for example. In some implementations, the historicalviewability data for each feature is updated hourly, with a 24-hourlookback window. In some implementations, the features can include adtag ID, website domain, client device operating system, bucketedrelative position x, bucketed relative position y, and so on. The modelgenerator uses the historical viewability rates of the features to builda logistic regression model that assigns different weights or modelcoefficients to each of the features. A new model can be computed everyday using the previous day's viewability rates and the modelcoefficients are updated. These model coefficients are then used alongwith the historical viewability data to compute the predictedviewability of all ad spaces for that day.

FIG. 4 is a flowchart of another example method for auctioning anavailable ad space based on a predicted viewability of the ad spaceusing an implementation of the system described herein. The methodbegins by receiving a notification of an ad space, the ad space beingpart of an ad space inventory of a seller, the ad space being forpresentation in a user interface of an application executing on a clientdevice of a user (402). The method sends, to the client device,instructions configured to be executed by the client device fordetermining viewability data of the ad space, the viewability datacomprising whether the ad space is within a viewport of the userinterface (404). The method receives, from the client device, theviewability data as determined by the instructions at a first timeinstance (406). The method calculates a predicted viewability such thatthe ad space is likely to be positioned within the viewport at a secondtime instance after the first time instance, wherein calculating thepredicted viewability uses a prediction model comprising one or morefeatures (408). The method sends, to a plurality of bidders, a bidrequest for bidding on an auction of the ad space, the bid requestcomprising the predicted viewability (410).

In one implementation, the data processing system described hereinimplements a viewable exchange in which impression inventory isavailable to impression buyers who only want to pay for impressions thatare viewable. Using the exchange, buyers are able to bid on impressionsthat are predicted to be viewable post-auction, with such predictionsbeing made as described herein or in some other manner. The operator ofthe present system can act as an intermediate party that charges apremium in order to take the risk that an impression ultimately may notbe viewable. More specifically, the system can accept viewable CPM(vCPM) bids, and if the expected CPM is calculated to be the highest bidin the auction, publishers are paid the expected CPM.

A transparent risk premium fee, in the form of a bid reduction, ischarged to take the risk and facilitate buying 100% viewable. The riskpremium fee is not a fee that is “charged” to either buyer or seller.Rather, in effect it reduces the eCPM bid that is submitted to theseller's auction, creating a difference between what is paid out tosellers and what it is likely to be paid in from buyers. In one example,the risk premium is 10% of the bid value after applying the predictedview rate; however, the risk premium can change based on the accuracy ofthe predicted view rate over time. For example, if the predicted viewrate model improves over time such that its predicted view ratecalculations are more accurate, the risk premium can be lowered. Themodel performance can be determined by comparing the predicted viewrates for impressions with log data indicating whether the impressionsended up being viewable. The risk premium can be recalculatedperiodically, e.g., weekly. In one implementation, the premium is notlogged or reported to either buyer or seller; the seller only sees theprice paid for their impression by the intermediate party.

Referring now to FIG. 5 , in the viewable exchange, buyer payments areeffectively separated from seller revenue by using the present system inthe transaction. On each impression, the system pays the impressionseller and seller-associated fees (SASC), regardless of whether theimpression was actually viewable. When, however, an impression isdetermined to be viewable, payment is collected from the impressionbuyer, net buyer-associated fees (BASC). In the example shown, a buyersubmits a $10 vCPM bid in an impression auction. A predicted view rate(PVR) is calculated for the impression that represents the likelihoodthat the impression will be viewable, and is applied to the initial bidvalue (here, the $10 bid is multiplied by a PVR of 50%, resulting in amodified bid of $5). The system then applies a risk premium (RP) to thebid, representing the consideration to the system provider in exchangefor taking the risk. Here, a 10% risk premium reduces the total bid to$4.50 eCPM. The net bid can then be calculated by multiplying theforegoing bid value by (1−BASC+SASC), which in this example results inan eCPM bid value of $3.60. For a second-price logic auction, thewinning bid (here, the $3.60 bid) is reduced to $0.01 above the secondhighest bid, resulting in a CPM bid/seller revenue of $3.01. The grossup value based on the seller value can be calculated byRevenue/(1−SASC+BASC)/(1−RP)/PVR. In the depicted example, the gross upvalue is 3.01/(1−0.2)/(1−0.10)/0.50=$8.36.

FIG. 5 also depicts a ledger 500 that tracks payments made to sellersand received from buyers (depending on whether an impression wasviewable or not viewable) and the resulting balance in a viewableexchange marketplace (VM) account. At any given moment, the balancecould be positive or negative, depending on the performance of theviewability prediction. In other words, if viewability predictions madeby the system tend to be inaccurate (i.e., impressions expected to beviewable are not viewable), more payments are made to sellers than arereceived from buyers, resulting in an imbalance irrespective of the riskpremium collected. Assuming perfect prediction and no risk premium, thebalance would be roughly $0.

Under some circumstances, the behavior of impression inventorytransacted in the viewable open exchange deviates from an exchange-wideinventory average. To correct any consistent over or under bias (i.e.,an over or under bias over several days or some other period of time) inthe prediction of viewable marketplace inventory, a gross adjustment canbe made to the predicted view rate of inventory identified asexperiencing such bias. In one implementation, the mechanism used is a“tag bias corrector,” by which a bias adjustment is made to the PVR ofad tags identified as having a consistent over or under bias.

In addition to potential issues with consistent over/under prediction,there may be inventory that is significantly less predictable than theplatform average (that is, higher standard deviation), even if there isno over/under prediction for the inventory on average. Because it isgenerally desirable only to take risk on inventory where there is areasonable confidence that the viewability can be predicted, suchunpredictable inventory can be identified by its deviation andblacklisted or otherwise removed from the marketplace.

Referring again to the system 100 of FIG. 1 , an ad auction using theviewability exchange will now be described. To summarize, for animpression buyer who wishes to only pay for impressions that areviewable (that is, to bid on a viewable CPM (vCPM) basis), the exchange(via transaction bus 110) will automatically translate the vCPM bidsfrom the buyer to CPM revenue for the seller, facilitating a transactionand bridging the currency gap between buyer and seller. The vCPM to CPMconversion rate can be calculated on every impression, based on thepredicted viewability of the impression (received from view predictorsubsystem 114), and the predicted viewability can be sent in a bidrequest to buyers. If a buyer chooses to bid on a vCPM basis, their bidwill be converted and entered into the seller's auction as a CPM bid,with normal auction mechanics applied by the transaction bus 110. Shouldthe bid win the auction, the seller will be paid the CPM price. Thebuyer's creative will be served to user application 104 with aviewability measurement script, and the buyer will only be charged thevCPM price if the impression is measured viewable. A default definitionof a viewable ad (50% of pixels in-view for 1 continuous second) and astandard viewability script can be used, or other definitions and/orscripts can be substituted.

The following describes in further detail how the auction works when abuyer submits a vCPM bid using the exchange. When an ad call is receivedfrom user application 104, the exchange (including transaction bus 110and view predictor subsystem 114) determines a prediction for thelikelihood that the impression will be measured viewable. If aprediction cannot be determined, then the impression will be ineligiblefor vCPM bids. There may be other reasons for not supporting vCPM bids.For example, monitoring may indicate that the prediction for the givenplacement is inaccurate, which may lead the placement to be markedineligible. If the viewability prediction is available, and othereligibility checks pass, then the exchange calculates a vCPM to CPMconversion rate, which is the viewability prediction multiplied by arisk adjustment, as described above.

On the bid request made by transaction bus 110 to console application118, the exchange indicates that it will accept vCPM bids. To do this,it defines an object in the request, which includes the payment type(vCPM), view, and the vCPM to CPM conversion rate. On a bid responsereceived from console application 118 by transaction bus 110, if aparticular buyer has chosen to submit a vCPM bid, it will be indicatedby a field in the response set to “view.” The bid price submitted aspart of the response will then be treated as a vCPM price. It ispossible for the vCPM bid to be rejected. In addition to standardrejection reasons (such as failing seller ad quality rules), monitoringmay indicate that the prediction for the given creative is inaccurate,resulting in the creative being marked ineligible. If the bid passesthese checks, then, for the purposes of running the auction, theexchange will convert the vCPM bid to CPM using the provided conversionrate. In one implementation, transaction bus 110 computes 2 parallelarrays to keep track of the bid value in each different form ofmeasurement (vCPM and eCPM, which differ by the conversion rate).

After bids are received, and any vCPM bids are converted to CPM, theauction can be executed by transaction bus 110 according to standardreal-time bidding mechanics, including, where applicable, second-pricelogic, private marketplaces, publisher floors, etc. If a CPM bidrepresenting a buyer's viewable bid is the winning price in the auction,it can be adjusted post-auction (for example, in a second price auction,it can be price-reduced to the second highest bid +$0.01), and theseller will receive this amount as revenue (minus standard auctionfees). The price-reduced CPM bid is converted back to a vCPM price,using the conversion rate. The buyer can be notified that it won theauction at the price-reduced vCPM price.

Following the auction, the winning buyer's creative is served to userapplication 104, along with a viewability measurement script. If thescript determines that the creative is viewable, an event will be sentto transaction bus 110 indicating that the buyer should be billed forthe impression at the price-reduced vCPM price. The exchange can log thetransaction using logging subsystem 122 and send a notification to thebuyer. If the measurement script determines that the ad is not viewable,or if it is unable to make the measurement, then the buyer is not billedfor the impression.

The nature of the auction mechanics, described above, creates adisconnect between how the buyer is charged and how the seller is paid.On any single impression, it is likely that the seller is paid eithersignificantly more or less than the buyer has been charged. Over manyimpressions, however, the difference between what is charged and what ispaid should converge. The key factor in this convergence is the accuracyof the predicted viewability rate; that is, the difference between theactual viewability rate and the predicted viewability rate. Toillustrate the impact of prediction accuracy on the amount billed versusthe amount paid, three different scenarios are described below: perfectprediction, under prediction, and over prediction. In the examplesbelow, a buyer bids $10 vCPM on supply with a 50% predicted viewabilityrate; the exchange converts the vCPM bid to a $5 eCPM bid; thesecond-highest bid in the auction is a $4 CPM bid; it is a second priceauction, so seller is paid $4.01 CPM; and the buyer is charged $8.02vCPM (that is, only if the impressions is measured viewable).

All else being equal, if the exchange perfectly predicts the viewabilityrate for a given piece of inventory, then the aggregate amount chargedto the vCPM buyer will match the aggregate amount paid to the seller. Onthe other hand, if the exchange over-predicts the viewability of theinventory, then the aggregate amount charged to the vCPM buyer is lessthan the aggregate amount paid to the seller For example, if theexchange predicts a 50% viewability rate, but the inventory has only a25% viewability rate, the exchange is left with a negative balance.Alternatively, if the exchange under-predicts the viewability of theinventory, then the aggregate amount charged to the vCPM buyer isgreater than the aggregate amount paid to the seller. As one example, ifthe exchange predicts a 50% viewability rate, but the inventory has a75% viewability rate, the exchange is left with a positive balance.

In some implementations, on every eligible impression, the exchangecalculates a vCPM to CPM conversion rate prior to sending bid requests.The conversion rate is then sent in the bid request, and, if any vCPMbids are returned, it is used to determine the eCPM price used for theseller's auction. To generate the conversation rate, the exchange usesthe predicted viewability rate of the given impression and apre-configured risk adjustment. The two rates can be multiplied togenerate the impression's conversion rate: conversion rate=predictedviewability rate*(1−risk factor).

The exchange acts as a financial buffer between buyer and seller, and,depending on the accuracy of the viewability prediction, the exchangemay have a positive or negative balance over time. An unexpectedmaterial inaccuracy in the viewability prediction has the potential tocause the exchange a significant financial loss. By measuring andmonitoring the overall accuracy of the viewability prediction, as wellas the historical performance of the marketplace, it is possible tomodel the financial risk to the exchange involved in the offering. Asnoted above, to compensate for the risk of financial loss, an adjustment(risk premium) is applied to the predicted viewability rate whengenerating the vCPM to CPM conversion rate. The adjustment creates adefault difference between the amount charged to vCPM buyers and theamount paid to sellers. In other words, in the scenario where there isperfect viewability prediction and all else is equal, the exchange willhave a net positive balance, equal to the adjustment percent. There is,however, no guarantee that the configured rate will be achieved, as thefinal net balance is a function of the viewability prediction accuracy.

In one implementation, publishers benefit from the presently disclosedtechniques by being able to negotiate and provide viewable ad deliveryguarantees. Traditionally, given knowledge of historical web page viewsand other tracking data, publishers are able to guarantee to advertisersthat they will receive a guaranteed number of impressions with aguaranteed CPM over a particular campaign or period of time. Here,however, historical and predicted view rates can be applied to guaranteea number of impressions that will be viewed (based on the particulardefinition of what makes an ad viewable) in negotiating a deal between apublisher and an advertiser. Such “VCPM guaranteed line items” arefulfilled by considering the viewable status of served impressions inconjunction with a pacing algorithm to ensure that a budget allocated tothe line item is spent fully and at an even pace (using, e.g., bids inreal-time bidding auctions for serving impressions) to achieve theguaranteed number of impressions over a flight period. The pacingalgorithm can, for example, increase bid amounts if an insufficientnumber of auctions are being won over a period of time (e.g., a bidmultiplier can be recalculated on a daily basis or other frequency).These bid amounts can also be calculated, as described above, based on apredicted view rate of the auctioned impression.

Generally, in a guaranteed ad serving system, line item selection isdetermined by a weighting mechanism where delivery against budget, asopposed to price, is the primary factor impacting selection. However,strictly basing line item selection on price ignores the need tomaintain even delivery of guarantees. In one implementation, to addressthis issue, bidders (e.g., console application 118) provide in their bidresponses to transaction bus 110 a valuation (referred to here as“priority CPM” or “pCPM”) that reflects the guaranteed line item's needfor fulfillment. Whether received from a bidder, calculated bytransaction bus 110, or otherwise, pCPM for a particular line item isdetermined based on pacing towards a goal. For example, the further aline item is from the goal for a particular defined interval, the higherthe pCPM should be. During an auction for an impression to be served toan impression consumer, guaranteed line items can be evaluated based ontheir priority level, and those with the highest priority level areevaluated first. If no line having a highest priority level is in needof the impression, line items in the next highest priority level areevaluated, and so on. Guaranteed line items that have already met theirbudget goals for a particular defined interval of time can be ignored(i.e., no bid for the impression is entered). Further, a randomnessfactor can be applied to ensure that the same line item is not alwaysselected within an interval where pCPM is static.

For the present guaranteed ad serving system for viewable impressions(rather than for all impressions, generally), the priority CPM conceptis similar, but must be translated into priority viewable CPM (pVCPM).Line items with viewable impression goals need to achieve viewableimpression guarantees instead of impression guarantees, which requiresmodifying the calculation of the bids needed to hit pacing goals. Inaddition, whereas every impression is worth the same forimpression-based goals, only viewable impressions are considered forviewable goals. To optimize yield, guaranteed line items should takeinto account the predicted viewability of each impression, whichinvolves certain considerations when it comes to pacing.

The goal of yield optimization for guaranteed delivery is to maximizethe revenue from non-guaranteed demand while hitting a delivery goal.This is a constrained optimization problem, in which the publisherattempts to minimize the cost of their guaranteed commitment. When thegoal is to reach an impression goal and all impressions contribute thesame towards hitting that goal, it can be shown that for a given budgetgoal (per minute, hour, day, etc.), there is a single optimal allocationprice, P*, that will maximize revenue. The publisher would prefer toserve guaranteed if and only if the highest non-guaranteed bid is belowP*. P* is, thus, the optimal bid, given the budget goal.

When some impressions contribute more than others, however, there is nolonger such a simple solution. When a line item has a viewableimpression goal, some impressions will be viewable and some will not.Suppose each impression, i, has a probability ν_(i) of being viewed. Itcan be shown that the optimal allocation rule for viewable impressionsgoals is to bid pVCPM=λ·ν_(i). That is, the optimal strategy is tomodify a base bid, lambda, by the predicted viewability of eachimpression. This implies that line items with viewable impression goalswould ignore any impression that has a predicted viewability of zero(because the product of lambda and the predicted viewability, andthereby pVCPM, would be zero).

In one implementation, lambda is calculated based on eligibility dataand win rate curves for each impression inventory chunk with a distinctν_(i). In another implementation, lambda is calculated based oneligibility data and win rate curves for each viewability value, whichcan be approximated with viewability buckets. This can be seen byworking through the viewed delivery equation, below. Suppose, forexample, that there are 10 discrete viewability buckets (0.1, 0.2, . . ., 1.0). Let E_(i) be the number of eligible impressions in bucket i andF_(i) be the win rate in bucket i. The viewed delivery for impressionsis then:Viewed Delivery=ΣE _(i) ·F _(i)(λ·ν_(i))·ν_(i)

That is, the viewed delivery is the sum of delivery in each bucketmultiplied by the viewability rate in each bucket. The delivery in eachbucket is its eligibility (the number of impressions multiplied by thewin rate when bidding lambda multiplied by viewability). Solving for thelambda that would hit a given target viewable impression goal can beachieved by computing win rate curves per viewability bucket.

Various techniques can be used to pace the spending of a budget fordelivering viewable advertisements. In one implementation, a standardguaranteed delivery algorithm can be updated to account for viewableimpressions, such that auction wins without views are not considered. Inone example, if the average viewability rate for a particular timeinterval t (since the previous calculation) is:

${\overset{¯}{V}}_{t} = \frac{\Sigma v_{it}}{{bids}\mspace{14mu}{since}\mspace{14mu}{last}\mspace{14mu}{update}}$then the viewable impression bid can be calculated as:

${pVCPM_{i}} = {{\frac{v_{i}}{{\overset{¯}{V}}_{t}} \cdot {base}}\mspace{14mu}{pCPM}_{t}}$Note that if there is no variation in viewability, then the bid price isthe base pCPM. Further, without average viewability rate in thedenominator, multiplying the bid by the predicted viewability wouldgenerally produce too low of a bid for a viewability less than 1

In another implementation, rather than solving for a bid price thatachieves a delivery goal, a pacing factor is calculated and applied to abid price. One such pacing algorithm for moderating spend using aproportional-integral-derivative (PID) controller is described in U.S.patent application Ser. No. 15/416,132, filed on Jan. 26, 2017, andentitled “Systems and Methods for Real-time Structured Object DataAggregation and Control Loop Feedback” (“the '132 application”) theentirety of which is incorporated by reference herein. In this case,starting with an arbitrary base bid, the controller can computationallysolve for the optimal lambda. That is, the bid is calculated as:pVCPM_(i)=ν_(i)·initial pCPM·pacing modifier

In general, the value of the initial pCPM is not critical, and can be avalue near the average estimated clear price (a bid price that is likelyto win most impressions based on historical bids and their success orfailure) for the publisher or other impression inventory seller.

In further implementations, to optimize yield given a viewability goal,a correct target should be set for delivery. To achieve the foregoing,eligibility and win rate curves are computed per viewability bucket, andthis information is used to computationally arrive at an optimal pacingtarget.

In one implementation, to enable the pacing techniques described above,the present system can incorporate a stream processing architecture toprovide bidders with pacing data used to calculate pCPM, as depicted inFIG. 6 . The stream processing architecture includes a streaming layer,execution layer, and processing layer. In one implementation, streaminglayer is a distributed publish/subscribe (pub/sub) and message queueingsystem 620, such as Apache Kafka™. For example, streaming layer canprovide at-least-once messaging guarantees to ensure no messages arelost when a fault occurs, and highly available partitions such that astream's partitions continue to be available even when a machine fails.Execution layer includes a cluster scheduler for allocating processesand executing jobs, such as Apache Hadoop® YARN (Yet Another ResourceNegotiator). Processing layer can include an application programminginterface (API) that provides for communication among the layers of thestream processing architecture and performs other tasks as describedherein.

As in YARN, execution layer can include a Resource Manager and a NodeManager. Individual machines each run a Node Manager, which isresponsible for launching processes on that machine. The ResourceManager communicates with Node Managers to instruct them which jobs toexecute. Processing layer can be used to inform the Resource Managerwhen a new job is to be created. The Resource Manager instructs a NodeManager to allocate space on a cluster and execute the requested job.

The use of the stream processing architecture depicted in FIG. 6 inconjunction with the system of FIG. 1 provides advantages over existingcomputing systems that perform similar tasks using batch processing oftransactions, which results in high latency. This combined systeminstead lowers latency by processing real-time transaction streams 602(e.g., streams with data arriving in real-time or periodically in shortperiods, such as every 5 or 10 seconds), and aggregating the real-timedata with data stored in high-volume, high-performance databases 604(e.g., made available through HPE Vertica). The combined system reduceslatency in provided input data to a pacing control function (caused byminutes-long delays in updating bidder pacing parameters after receivingauction results) by having only a simple pacing control mechanism inbidders 610 and locating more complex pacing logic into an externalpacing subsystem. Accordingly, the time between auction win notification(identified as “T”) and the application of a new pacing strategy by abidder 610 can be reduced to 60 seconds or less (T−60).

The architecture of FIG. 6 provides a pull mechanism for pacing records.More specifically, pacing record publisher 624 can subscribe to thepub/sub and message queueing system 620 (e.g., Kafka) in the streaminglayer and push the received records to bidders 610 at a specified rate(e.g., T−60 seconds). Algorithms W, X, Y, and Z (616) representadditional pacing controllers that can be implemented without impact ordependency on downstream components (e.g., pacing record publisher 624,bidder 610). In one implementation, one such algorithm is a bid pricepacing algorithm such as that described in U.S. patent application Ser.No. 14/561,615, filed on Dec. 5, 2014, and entitled “Modulating BudgetSpending Pace for Online Advertising Auction by Adjusting Bid Prices,”the entirety of which is incorporated by reference. In anotherimplementation, another algorithm includes a day part optimizationalgorithm that examines historical conversion data and weights anadvertising budget across different hours of the week according to theirrespective conversion rates.

To reduce the latency and enable a quick feedback loop from pacingcontroller to bidder 610, Lambda architecture-based data processing 630can be used. The data layer can create data streams from batch-retrieveddata stored in a database 634 (e.g., cumulative spend data, budget, timezone information, and other data) and combine them with a real-timetransaction stream to compute current spend for advertising objects orelements (campaign, advertiser, etc.) as of a particular time. A spendaggregation job (in the format of, e.g., an Apache Samza job) is thencreated at the execution subsystem that consumes the streams toaggregate the cumulative spend, booked revenue and delivered impressionsfor the objects and streams the results to the pub/sub subsystem 620periodically (e.g., every 10 seconds).

Now referring to FIG. 7 , the data layer can create data streams(opt_agg_cumulative_spend and opt_agg_budget_object_timezone) frombatch-retrieved data stored in a database (e.g., cumulative spend data,budget, time zone information, and other data as described below) andcombine them with a real-time transaction stream(agg_dw_transactions_by_buyer) to compute current spend for advertisingobjects or elements (campaign, advertiser, etc.) as of a particulartime. A Spend Aggregation job (in the format of, e.g., an Apache Samzajob) is then created at the execution subsystem that consumes theagg_dw_transactions_by_buyer and opt_agg_cumulative_spend streams toaggregate the cumulative spend, booked revenue and delivered impressionsfor the objects and streams the results to the pub/sub subsystemperiodically (e.g., every 10 seconds).

The Spend Aggregation job can use a local key-value (kv) store toaggregate cumulative spend, booked revenue, and delivered impressions byan object identifier and/or object type. As one example, the job canmaintain three tables in the key-value store: (1) current day cumulativespend, (2) current hour spend, and (3) previous hour spend. Periodically(e.g., every five seconds), the job merges the spend data from the threetables and writes a message with data on current spend(opt_agg_current_spend) to the message queuing subsystem. When hourlyspend data is available, a cumulative spend stream producer job canaggregate the spend, booked revenue, and impressions for the current day(at campaign, campaign group and insertion order) and publish a messagewith cumulative spend data (opt_agg_cumulative_spend) to the messagingqueue subsystem. This job consume three streams: (1) the transactionstream (agg_dw_transactions_by_buyer), which is fed by the real-timeprocessing system, (2) the cumulative spend stream(opt_agg_cumulative_spend), and the object level time zone stream(opt_budget_object_timezone), the latter two of which can be fed byVertica stream producers from batched data. Object level time zone isused to report the metrics at object local time zone scope. Settings formessage priority can be enabled to prioritize the real-time stream overthe batch stream, so that the real-time processing doesn't slow down ifthere is a sudden burst of data on the batch stream.

The real-time transaction stream includes joined auction data from acomplex event processor that provides real-time structured object dataaggregation. FIG. 8 depicts a logical view of real-time structuredobject data aggregation using join scripts. Joined data can relate, forexample, to impressions, preempts, bids, and/or views. In oneimplementation, these scripts perform data joining as follows.

Rows of data are received at instances of the complex event processorand are routed to a load balancing queue to enable better utilization ofavailable processing resources. Each table is written into ade-duplication queue where the rows are de-duped based on a uniqueidentifier (e.g., a 64-bit identifier associated with an onlineadvertising auction). Rows that pass the dupe check (non-dupes) havetheir time-to-live (TTL) field read and are routed to another scriptlayer based on the incoming TTL. Rows with the same TTL will arrive atthe same TTL layer together. Each TTL script layer can have atime-limited (e.g., 300 second) in-memory join, so as to avoid writingevery single row to disk. For a 300-second TTL, rows are evicted after300 seconds and flow back to data storage. For a higher TTL threshold(e.g., 750 seconds and upwards), unjoined rows are written to disk. If afull join occurs before the TTL expires, the rows are flushed at thetime of the full join. Otherwise, the rows are flushed when TTL expires.The TTLs in FIG. 8 (300, 750, 1800, and so on) are in seconds.

Implementations of the subject matter and the operations described inthis specification can be implemented in digital electronic circuitry,or in computer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Implementations of the subjectmatter described in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on computer storage medium for execution by, or tocontrol the operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. A computer storage medium canbe, or be included in, a computer-readable storage device, acomputer-readable storage substrate, a random or serial access memoryarray or device, or a combination of one or more of them. Moreover,while a computer storage medium is not a propagated signal, a computerstorage medium can be a source or destination of computer programinstructions encoded in an artificially-generated propagated signal. Thecomputer storage medium can also be, or be included in, one or moreseparate physical components or media (e.g., multiple CDs, disks, orother storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application-specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languageresource), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub-programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read-only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto-optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.Devices suitable for storing computer program instructions and datainclude all forms of non-volatile memory, media and memory devices,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending resources to and receiving resources from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back-endcomponent, e.g., as a data server, or that includes a middlewarecomponent, e.g., an application server, or that includes a front-endcomponent, e.g., a client computer having a graphical user interface ora Web browser through which a user can interact with an implementationof the subject matter described in this specification, or anycombination of one or more such back-end, middleware, or front-endcomponents. The components of the system can be interconnected by anyform or medium of digital data communication, e.g., a communicationnetwork. Examples of communication networks include a local area network(“LAN”) and a wide area network (“WAN”), an inter-network (e.g., theInternet), and peer-to-peer networks (e.g., ad hoc peer-to-peernetworks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular implementations of particularinventions. Certain features that are described in this specification inthe context of separate implementations can also be implemented incombination in a single implementation. Conversely, various featuresthat are described in the context of a single implementation can also beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the device, facilitate performance of operations,the operations comprising: transmitting an instruction to thecommunication device, wherein the instruction causes the communicationdevice to determine viewability data of advertisement space presented ina user interface of the communication device, wherein the viewabilitydata indicates whether the advertisement space is within an areadisplayed in the user interface of the communication device; receivingthe viewability data of the advertisement space during a first timeinterval from the communication device; determining, based on theviewability data, a predicted viewability of the advertisement spaceduring a second time interval, wherein the predicted viewabilityincludes a likelihood of the advertisement space being positioned in atleast a part of the area during the second time interval, and whereinthe second time interval is subsequent to the first time interval; andsending a first bid request to each of a group of computing devices,wherein each computing device of the group of computing devices isassociated with a bidder, wherein the first bid request comprises thepredicted viewability of the advertisement space during the second timeinterval.
 2. The device of claim 1, wherein the operations comprise:receiving a bid from each of the group of computing devices resulting ina first group of bids; and selecting a first bid from the first group ofbids, wherein the first bid is a highest bid from among the first groupof bids.
 3. The device of claim 1, wherein the first bid request isassociated with a first bid, wherein the sending of the first bidrequest to each of the group of computing devices comprises sending afirst bid discount associated with the bid.
 4. The device of claim 3,wherein the first bid discount is based on the predicted viewability ofthe advertisement space.
 5. The device of claim 3, wherein the first biddiscount is based on a historical viewability of the advertisementspace.
 6. The device of claim 3, wherein the operations comprise:determining an actual viewability of the advertisement space;determining an accuracy of the predicted viewability of theadvertisement space with the actual viewability of the advertisementspace resulting in a first determination; computing a first payment fora first bidder associated with the first bid based on the first bid, thefirst bid discount, and the first determination; and providing the firstpayment to the first bidder.
 7. The device of claim 6, wherein theoperations comprise adjusting the first bid discount to a second biddiscount based on the first determination.
 8. The device of claim 7,wherein the operations comprise sending a second bid request to each ofthe group of computing devices, wherein the second bid request comprisesthe predicted viewability of the advertisement space and the second biddiscount during a third time interval.
 9. The device of claim 8, whereinthe operations comprise: receiving a bid from each of the group ofcomputing devices resulting in a second group of bids; and selecting asecond bid from the second group of bids, wherein the second bid is ahighest bid from among the second group of bids.
 10. The device of claim9, wherein the operations comprise: determining an updated actualviewability of the advertisement space; determining an updated accuracyof the predicted viewability of the advertisement space with the updatedactual viewability of the advertisement space resulting in a seconddetermination; computing a second payment for a second bidder associatedwith the second bid based on the second bid, the second bid discount,and the second determination; and providing the second payment to thesecond bidder.
 11. A non-transitory, machine-readable medium, comprisingexecutable instructions that, when executed by a processing systemincluding a processor, facilitate performance of operations, theoperations comprising: transmitting an instruction to the communicationdevice, wherein the instruction causes the communication device todetermine viewability data of advertisement space presented in a userinterface of the communication device, wherein the viewability dataindicates whether the advertisement space is within an area displayed inthe user interface of the communication device; receiving theviewability data of the advertisement space during a first time intervalfrom the communication device; determining, based on the viewabilitydata, a predicted viewability of the advertisement space during a secondtime interval, wherein the predicted viewability includes a likelihoodof the advertisement space being positioned in at least a part of thearea during the second time interval, and wherein the second timeinterval is subsequent to the first time interval; and sending a firstbid request to each of a group of computing devices, wherein eachcomputing device of the group of computing devices is associated with abidder, wherein the first bid request comprises the predictedviewability of the advertisement space during the second time intervaland a first bid discount.
 12. The non-transitory, machine-readablemedium of claim 11, wherein the first bid discount is based on thepredicted viewability of the advertisement space.
 13. Thenon-transitory, machine-readable medium of claim 11, wherein the firstbid discount is based on a historical viewability of the advertisementspace.
 14. The non-transitory, machine-readable medium of claim 11,wherein the operations comprise: receiving a bid from each of the groupof computing devices resulting in a first group of bids; and selecting afirst bid from the first group of bids, wherein the first bid is ahighest bid from among the first group of bids.
 15. The non-transitory,machine-readable medium of claim 11, wherein the operations comprise:determining an actual viewability of the advertisement space;determining an accuracy of the predicted viewability of theadvertisement space with the actual viewability of the advertisementspace resulting in a first determination; computing a first payment fora first bidder associated with the first bid based on the first bid, thefirst bid discount, and the first determination; and providing the firstpayment to the first bidder.
 16. The non-transitory, machine-readablemedium of claim 15, wherein the operations comprise adjusting the firstbid discount to a second bid discount based on the determination. 17.The non-transitory, machine-readable medium of claim 16, wherein theoperations comprise sending a second bid request to each of the group ofcomputing devices, wherein the second bid request comprises thepredicted viewability of the advertisement space and the second biddiscount during a third time interval.
 18. The non-transitory,machine-readable medium of claim 17, wherein the operations comprise:receiving a bid from each of the group of computing devices resulting ina second group of bids; and selecting a second bid from the second groupof bids, wherein the second bid is a highest bid from among the secondgroup of bids.
 19. The non-transitory, machine-readable medium of claim18, wherein the operations comprise: determining an updated actualviewability of the advertisement space; determining an updated accuracyof the predicted viewability of the advertisement space with the updatedactual viewability of the advertisement space resulting in a seconddetermination; computing a second payment for a second bidder associatedwith the second bid based on the second bid, the second bid discount,and the second determination; and providing the second payment to thesecond bidder.
 20. A method, comprising: transmitting by a processingsystem, an instruction to the communication device, wherein theinstruction causes the communication device to determine viewabilitydata of advertisement space presented in a user interface of thecommunication device, wherein the viewability data indicates whether theadvertisement space is within an area displayed in the user interface ofthe communication device; receiving, by the processing system, theviewability data of the advertisement space during a first time intervalfrom the communication device; determining, based on the viewabilitydata, by the processing system, a predicted viewability of theadvertisement space during a second time interval, wherein the predictedviewability includes a likelihood of the advertisement space beingpositioned in at least a part of the area during the second timeinterval, and wherein the second time interval is subsequent to thefirst time interval; sending, by the processing system, a first bidrequest to each of a group of computing devices, wherein each computingdevice of the group of computing devices is associated with a bidder,wherein the first bid request comprises the predicted viewability of theadvertisement space during the second time interval and a first biddiscount; receiving, by the processing system, a bid from each of thegroup of computing devices resulting in a first group of bids;selecting, by the processing system, a first bid from the first group ofbids, wherein the first bid is a highest bid from among the first groupof bids; determining, by the processing system, an actual viewability ofthe advertisement space; determining, by the processing system, anaccuracy of the predicted viewability of the advertisement space withthe actual viewability of the advertisement space resulting in a firstdetermination; computing, by the processing system, a first payment fora first bidder associated with the first bid based on the first bid, thefirst bid discount, and the first determination; and providing, by theprocessing system, first payment to the first bidder.